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Socioeconomic Patterns of Twitter User Activity

Entropy (Basel, Switzerland), 2021-06, Vol.23 (6), p.780 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1099-4300 ;EISSN: 1099-4300 ;DOI: 10.3390/e23060780 ;PMID: 34205367

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  • Title:
    Socioeconomic Patterns of Twitter User Activity
  • Author: Abitbol, Jacob Levy ; Morales, Alfredo J.
  • Subjects: Application programming interface ; Behavior ; data analysis ; Demographics ; human behavior ; Income ; Language ; Machine learning ; Mobility ; Multiculturalism & pluralism ; Neighborhoods ; Quantiles ; Segregation ; social media ; Social networks ; Socioeconomic factors ; socioeconomic status ; Verbal communication
  • Is Part Of: Entropy (Basel, Switzerland), 2021-06, Vol.23 (6), p.780
  • Description: Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their successful collection. In this study, we use data from social media to expose how behavioral patterns in different socioeconomic groups can be used to infer an individual’s income. In particular, we look at the way people explore cities and use topics of conversation online as a means of inferring individual socioeconomic status. Privacy is preserved by using anonymized data, and abstracting human mobility and online conversation topics as aggregated high-dimensional vectors. We show that mobility and hashtag activity are good predictors of income and that the highest and lowest socioeconomic quantiles have the most differentiated behavior across groups.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1099-4300
    EISSN: 1099-4300
    DOI: 10.3390/e23060780
    PMID: 34205367
  • Source: GFMER Free Medical Journals
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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